Overview

Dataset statistics

Number of variables21
Number of observations21436
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 MiB
Average record size in memory176.0 B

Variable types

Numeric17
DateTime1
Categorical3

Alerts

price is highly overall correlated with grade and 3 other fieldsHigh correlation
bedrooms is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
bathrooms is highly overall correlated with bedrooms and 6 other fieldsHigh correlation
sqft_living is highly overall correlated with bathrooms and 5 other fieldsHigh correlation
sqft_lot is highly overall correlated with sqft_lot15High correlation
floors is highly overall correlated with bathrooms and 3 other fieldsHigh correlation
grade is highly overall correlated with bathrooms and 5 other fieldsHigh correlation
sqft_above is highly overall correlated with bathrooms and 6 other fieldsHigh correlation
yr_built is highly overall correlated with bathrooms and 1 other fieldsHigh correlation
zipcode is highly overall correlated with longHigh correlation
long is highly overall correlated with zipcodeHigh correlation
sqft_living15 is highly overall correlated with bathrooms and 4 other fieldsHigh correlation
sqft_lot15 is highly overall correlated with sqft_lotHigh correlation
condition is highly overall correlated with yr_builtHigh correlation
sqft_basement is highly overall correlated with price and 3 other fieldsHigh correlation
lat is highly overall correlated with zipcodeHigh correlation
view is highly overall correlated with waterfrontHigh correlation
waterfront is highly overall correlated with viewHigh correlation
waterfront is highly imbalanced (93.6%)Imbalance
view is highly imbalanced (72.2%)Imbalance
id has unique valuesUnique
sqft_basement has 13015 (60.7%) zerosZeros
yr_renovated has 20526 (95.8%) zerosZeros

Reproduction

Analysis started2024-11-27 12:43:45.293331
Analysis finished2024-11-27 12:44:15.486148
Duration30.19 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIQUE 

Distinct21436
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5807653 × 109
Minimum1000102
Maximum9.9000002 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2024-11-27T20:44:15.617381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1000102
5-th percentile5.1000319 × 108
Q12.1237001 × 109
median3.9049212 × 109
Q37.3086751 × 109
95-th percentile9.2973006 × 109
Maximum9.9000002 × 109
Range9.8990001 × 109
Interquartile range (IQR)5.184975 × 109

Descriptive statistics

Standard deviation2.8765896 × 109
Coefficient of variation (CV)0.6279714
Kurtosis-1.2605654
Mean4.5807653 × 109
Median Absolute Deviation (MAD)2.4027999 × 109
Skewness0.24323134
Sum9.8193286 × 1013
Variance8.2747679 × 1018
MonotonicityNot monotonic
2024-11-27T20:44:15.742163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7129300520 1
 
< 0.1%
8929000230 1
 
< 0.1%
9543000205 1
 
< 0.1%
8137500730 1
 
< 0.1%
104500730 1
 
< 0.1%
7575610760 1
 
< 0.1%
629800540 1
 
< 0.1%
7215730120 1
 
< 0.1%
2064800610 1
 
< 0.1%
3577300040 1
 
< 0.1%
Other values (21426) 21426
> 99.9%
ValueCountFrequency (%)
1000102 1
< 0.1%
1200019 1
< 0.1%
1200021 1
< 0.1%
2800031 1
< 0.1%
3600057 1
< 0.1%
3600072 1
< 0.1%
3800008 1
< 0.1%
5200087 1
< 0.1%
6200017 1
< 0.1%
7200080 1
< 0.1%
ValueCountFrequency (%)
9900000190 1
< 0.1%
9895000040 1
< 0.1%
9842300540 1
< 0.1%
9842300485 1
< 0.1%
9842300095 1
< 0.1%
9842300036 1
< 0.1%
9839301165 1
< 0.1%
9839301060 1
< 0.1%
9839301055 1
< 0.1%
9839300875 1
< 0.1%

date
Date

Distinct372
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size334.9 KiB
Minimum2014-05-02 00:00:00
Maximum2015-05-27 00:00:00
2024-11-27T20:44:15.866803image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:16.342792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

price
Real number (ℝ)

HIGH CORRELATION 

Distinct3997
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean541649.96
Minimum75000
Maximum7700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2024-11-27T20:44:16.468431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum75000
5-th percentile213500
Q1324866
median450000
Q3645000
95-th percentile1160000
Maximum7700000
Range7625000
Interquartile range (IQR)320134

Descriptive statistics

Standard deviation367314.93
Coefficient of variation (CV)0.67814078
Kurtosis34.725226
Mean541649.96
Median Absolute Deviation (MAD)150000
Skewness4.0361072
Sum1.1610809 × 1010
Variance1.3492026 × 1011
MonotonicityNot monotonic
2024-11-27T20:44:16.617706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450000 172
 
0.8%
350000 167
 
0.8%
550000 157
 
0.7%
500000 151
 
0.7%
425000 149
 
0.7%
325000 146
 
0.7%
400000 144
 
0.7%
375000 137
 
0.6%
300000 131
 
0.6%
525000 128
 
0.6%
Other values (3987) 19954
93.1%
ValueCountFrequency (%)
75000 1
< 0.1%
78000 1
< 0.1%
80000 1
< 0.1%
81000 1
< 0.1%
82500 1
< 0.1%
83000 1
< 0.1%
84000 1
< 0.1%
85000 1
< 0.1%
89000 1
< 0.1%
89950 1
< 0.1%
ValueCountFrequency (%)
7700000 1
< 0.1%
7062500 1
< 0.1%
6885000 1
< 0.1%
5570000 1
< 0.1%
5350000 1
< 0.1%
5300000 1
< 0.1%
5110800 1
< 0.1%
4668000 1
< 0.1%
4500000 1
< 0.1%
4489000 1
< 0.1%

bedrooms
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3715712
Minimum0
Maximum33
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2024-11-27T20:44:16.726301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum33
Range33
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.92920466
Coefficient of variation (CV)0.27559989
Kurtosis49.638224
Mean3.3715712
Median Absolute Deviation (MAD)1
Skewness1.9898602
Sum72273
Variance0.8634213
MonotonicityNot monotonic
2024-11-27T20:44:16.819307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 9731
45.4%
4 6849
32.0%
2 2736
 
12.8%
5 1586
 
7.4%
6 265
 
1.2%
1 194
 
0.9%
7 38
 
0.2%
0 13
 
0.1%
8 13
 
0.1%
9 6
 
< 0.1%
Other values (3) 5
 
< 0.1%
ValueCountFrequency (%)
0 13
 
0.1%
1 194
 
0.9%
2 2736
 
12.8%
3 9731
45.4%
4 6849
32.0%
5 1586
 
7.4%
6 265
 
1.2%
7 38
 
0.2%
8 13
 
0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
33 1
 
< 0.1%
11 1
 
< 0.1%
10 3
 
< 0.1%
9 6
 
< 0.1%
8 13
 
0.1%
7 38
 
0.2%
6 265
 
1.2%
5 1586
 
7.4%
4 6849
32.0%
3 9731
45.4%

bathrooms
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1173493
Minimum0
Maximum8
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2024-11-27T20:44:16.920290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.75
median2.25
Q32.5
95-th percentile3.5
Maximum8
Range8
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.76991279
Coefficient of variation (CV)0.36362105
Kurtosis1.2914875
Mean2.1173493
Median Absolute Deviation (MAD)0.5
Skewness0.51018057
Sum45387.5
Variance0.5927657
MonotonicityNot monotonic
2024-11-27T20:44:17.031578image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2.5 5355
25.0%
1 3795
17.7%
1.75 3020
14.1%
2.25 2031
 
9.5%
2 1913
 
8.9%
1.5 1430
 
6.7%
2.75 1182
 
5.5%
3 747
 
3.5%
3.5 729
 
3.4%
3.25 586
 
2.7%
Other values (20) 648
 
3.0%
ValueCountFrequency (%)
0 10
 
< 0.1%
0.5 4
 
< 0.1%
0.75 71
 
0.3%
1 3795
17.7%
1.25 9
 
< 0.1%
1.5 1430
 
6.7%
1.75 3020
14.1%
2 1913
 
8.9%
2.25 2031
 
9.5%
2.5 5355
25.0%
ValueCountFrequency (%)
8 2
 
< 0.1%
7.75 1
 
< 0.1%
7.5 1
 
< 0.1%
6.75 2
 
< 0.1%
6.5 2
 
< 0.1%
6.25 2
 
< 0.1%
6 6
< 0.1%
5.75 4
 
< 0.1%
5.5 10
< 0.1%
5.25 13
0.1%

sqft_living
Real number (ℝ)

HIGH CORRELATION 

Distinct1038
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2082.7049
Minimum290
Maximum13540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2024-11-27T20:44:17.153459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile940
Q11430
median1920
Q32550
95-th percentile3770
Maximum13540
Range13250
Interquartile range (IQR)1120

Descriptive statistics

Standard deviation919.14647
Coefficient of variation (CV)0.44132342
Kurtosis5.2490757
Mean2082.7049
Median Absolute Deviation (MAD)550
Skewness1.471021
Sum44644863
Variance844830.23
MonotonicityNot monotonic
2024-11-27T20:44:17.287601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300 136
 
0.6%
1440 133
 
0.6%
1400 132
 
0.6%
1660 128
 
0.6%
1800 128
 
0.6%
1820 127
 
0.6%
1560 124
 
0.6%
1010 124
 
0.6%
1480 122
 
0.6%
1540 122
 
0.6%
Other values (1028) 20160
94.0%
ValueCountFrequency (%)
290 1
< 0.1%
370 1
< 0.1%
380 1
< 0.1%
384 1
< 0.1%
390 2
< 0.1%
410 1
< 0.1%
420 2
< 0.1%
430 1
< 0.1%
440 1
< 0.1%
460 1
< 0.1%
ValueCountFrequency (%)
13540 1
< 0.1%
12050 1
< 0.1%
10040 1
< 0.1%
9890 1
< 0.1%
9640 1
< 0.1%
9200 1
< 0.1%
8670 1
< 0.1%
8020 1
< 0.1%
8010 1
< 0.1%
8000 1
< 0.1%

sqft_lot
Real number (ℝ)

HIGH CORRELATION 

Distinct9782
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15135.638
Minimum520
Maximum1651359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2024-11-27T20:44:17.411820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile1800
Q15040
median7614
Q310696.25
95-th percentile43560
Maximum1651359
Range1650839
Interquartile range (IQR)5656.25

Descriptive statistics

Standard deviation41538.621
Coefficient of variation (CV)2.7444248
Kurtosis284.08354
Mean15135.638
Median Absolute Deviation (MAD)2615.5
Skewness13.043673
Sum3.2444753 × 108
Variance1.725457 × 109
MonotonicityNot monotonic
2024-11-27T20:44:17.534910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 355
 
1.7%
6000 286
 
1.3%
4000 249
 
1.2%
7200 218
 
1.0%
4800 118
 
0.6%
7500 118
 
0.6%
4500 112
 
0.5%
8400 109
 
0.5%
9600 108
 
0.5%
3600 102
 
0.5%
Other values (9772) 19661
91.7%
ValueCountFrequency (%)
520 1
< 0.1%
572 1
< 0.1%
600 1
< 0.1%
609 1
< 0.1%
635 1
< 0.1%
638 1
< 0.1%
649 2
< 0.1%
651 1
< 0.1%
675 1
< 0.1%
676 1
< 0.1%
ValueCountFrequency (%)
1651359 1
< 0.1%
1164794 1
< 0.1%
1074218 1
< 0.1%
1024068 1
< 0.1%
982998 1
< 0.1%
982278 1
< 0.1%
920423 1
< 0.1%
881654 1
< 0.1%
871200 2
< 0.1%
843309 1
< 0.1%

floors
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.496198
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2024-11-27T20:44:17.633589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.54038838
Coefficient of variation (CV)0.36117438
Kurtosis-0.49077396
Mean1.496198
Median Absolute Deviation (MAD)0.5
Skewness0.61047946
Sum32072.5
Variance0.2920196
MonotonicityNot monotonic
2024-11-27T20:44:17.723219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 10559
49.3%
2 8209
38.3%
1.5 1888
 
8.8%
3 611
 
2.9%
2.5 161
 
0.8%
3.5 8
 
< 0.1%
ValueCountFrequency (%)
1 10559
49.3%
1.5 1888
 
8.8%
2 8209
38.3%
2.5 161
 
0.8%
3 611
 
2.9%
3.5 8
 
< 0.1%
ValueCountFrequency (%)
3.5 8
 
< 0.1%
3 611
 
2.9%
2.5 161
 
0.8%
2 8209
38.3%
1.5 1888
 
8.8%
1 10559
49.3%

waterfront
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size334.9 KiB
0
21273 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21436
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21273
99.2%
1 163
 
0.8%

Length

2024-11-27T20:44:17.817834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T20:44:17.908149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 21273
99.2%
1 163
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 21273
99.2%
1 163
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21436
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21273
99.2%
1 163
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21436
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21273
99.2%
1 163
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21436
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21273
99.2%
1 163
 
0.8%

view
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size334.9 KiB
0
19320 
2
 
962
3
 
507
1
 
331
4
 
316

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21436
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19320
90.1%
2 962
 
4.5%
3 507
 
2.4%
1 331
 
1.5%
4 316
 
1.5%

Length

2024-11-27T20:44:17.999168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T20:44:18.089221image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 19320
90.1%
2 962
 
4.5%
3 507
 
2.4%
1 331
 
1.5%
4 316
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 19320
90.1%
2 962
 
4.5%
3 507
 
2.4%
1 331
 
1.5%
4 316
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21436
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19320
90.1%
2 962
 
4.5%
3 507
 
2.4%
1 331
 
1.5%
4 316
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21436
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19320
90.1%
2 962
 
4.5%
3 507
 
2.4%
1 331
 
1.5%
4 316
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21436
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19320
90.1%
2 962
 
4.5%
3 507
 
2.4%
1 331
 
1.5%
4 316
 
1.5%

condition
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size334.9 KiB
3
13911 
4
5645 
5
1687 
2
 
164
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21436
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row5
5th row3

Common Values

ValueCountFrequency (%)
3 13911
64.9%
4 5645
26.3%
5 1687
 
7.9%
2 164
 
0.8%
1 29
 
0.1%

Length

2024-11-27T20:44:18.191807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T20:44:18.280427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3 13911
64.9%
4 5645
26.3%
5 1687
 
7.9%
2 164
 
0.8%
1 29
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 13911
64.9%
4 5645
26.3%
5 1687
 
7.9%
2 164
 
0.8%
1 29
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21436
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 13911
64.9%
4 5645
26.3%
5 1687
 
7.9%
2 164
 
0.8%
1 29
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21436
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 13911
64.9%
4 5645
26.3%
5 1687
 
7.9%
2 164
 
0.8%
1 29
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21436
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 13911
64.9%
4 5645
26.3%
5 1687
 
7.9%
2 164
 
0.8%
1 29
 
0.1%

grade
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6617373
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2024-11-27T20:44:18.370229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q17
median7
Q38
95-th percentile10
Maximum13
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1742565
Coefficient of variation (CV)0.15326243
Kurtosis1.1902695
Mean7.6617373
Median Absolute Deviation (MAD)1
Skewness0.77035741
Sum164237
Variance1.3788783
MonotonicityNot monotonic
2024-11-27T20:44:18.459913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 8896
41.5%
8 6044
28.2%
9 2606
 
12.2%
6 1995
 
9.3%
10 1130
 
5.3%
11 396
 
1.8%
5 234
 
1.1%
12 89
 
0.4%
4 29
 
0.1%
13 13
 
0.1%
Other values (2) 4
 
< 0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
3 3
 
< 0.1%
4 29
 
0.1%
5 234
 
1.1%
6 1995
 
9.3%
7 8896
41.5%
8 6044
28.2%
9 2606
 
12.2%
10 1130
 
5.3%
11 396
 
1.8%
ValueCountFrequency (%)
13 13
 
0.1%
12 89
 
0.4%
11 396
 
1.8%
10 1130
 
5.3%
9 2606
 
12.2%
8 6044
28.2%
7 8896
41.5%
6 1995
 
9.3%
5 234
 
1.1%
4 29
 
0.1%

sqft_above
Real number (ℝ)

HIGH CORRELATION 

Distinct946
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1790.9604
Minimum290
Maximum9410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2024-11-27T20:44:18.563572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile850
Q11200
median1560
Q32220
95-th percentile3400
Maximum9410
Range9120
Interquartile range (IQR)1020

Descriptive statistics

Standard deviation829.02649
Coefficient of variation (CV)0.46289492
Kurtosis3.3950819
Mean1790.9604
Median Absolute Deviation (MAD)450
Skewness1.4442209
Sum38391028
Variance687284.92
MonotonicityNot monotonic
2024-11-27T20:44:18.677266image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300 210
 
1.0%
1010 204
 
1.0%
1200 203
 
0.9%
1220 186
 
0.9%
1140 183
 
0.9%
1400 180
 
0.8%
1060 177
 
0.8%
1180 177
 
0.8%
1340 174
 
0.8%
1250 173
 
0.8%
Other values (936) 19569
91.3%
ValueCountFrequency (%)
290 1
< 0.1%
370 1
< 0.1%
380 1
< 0.1%
384 1
< 0.1%
390 2
< 0.1%
410 1
< 0.1%
420 2
< 0.1%
430 1
< 0.1%
440 1
< 0.1%
460 1
< 0.1%
ValueCountFrequency (%)
9410 1
< 0.1%
8860 1
< 0.1%
8570 1
< 0.1%
8020 1
< 0.1%
7880 1
< 0.1%
7850 1
< 0.1%
7680 1
< 0.1%
7420 1
< 0.1%
7320 1
< 0.1%
6720 1
< 0.1%

sqft_basement
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct306
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.7445
Minimum0
Maximum4820
Zeros13015
Zeros (%)60.7%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2024-11-27T20:44:18.792855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3560
95-th percentile1190
Maximum4820
Range4820
Interquartile range (IQR)560

Descriptive statistics

Standard deviation442.78198
Coefficient of variation (CV)1.5177047
Kurtosis2.7119878
Mean291.7445
Median Absolute Deviation (MAD)0
Skewness1.5768731
Sum6253835
Variance196055.88
MonotonicityNot monotonic
2024-11-27T20:44:18.921220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13015
60.7%
600 220
 
1.0%
700 215
 
1.0%
500 211
 
1.0%
800 206
 
1.0%
400 184
 
0.9%
1000 147
 
0.7%
900 143
 
0.7%
300 141
 
0.7%
480 106
 
0.5%
Other values (296) 6848
31.9%
ValueCountFrequency (%)
0 13015
60.7%
10 1
 
< 0.1%
20 1
 
< 0.1%
40 4
 
< 0.1%
50 11
 
0.1%
60 10
 
< 0.1%
65 1
 
< 0.1%
70 7
 
< 0.1%
80 20
 
0.1%
90 21
 
0.1%
ValueCountFrequency (%)
4820 1
< 0.1%
4130 1
< 0.1%
3500 1
< 0.1%
3480 1
< 0.1%
3260 1
< 0.1%
3000 1
< 0.1%
2850 1
< 0.1%
2810 1
< 0.1%
2730 1
< 0.1%
2720 1
< 0.1%

yr_built
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.0984
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2024-11-27T20:44:19.037159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1915
Q11952
median1975
Q31997
95-th percentile2011
Maximum2015
Range115
Interquartile range (IQR)45

Descriptive statistics

Standard deviation29.385277
Coefficient of variation (CV)0.014908072
Kurtosis-0.65434573
Mean1971.0984
Median Absolute Deviation (MAD)23
Skewness-0.47457738
Sum42252466
Variance863.49449
MonotonicityNot monotonic
2024-11-27T20:44:19.159620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 559
 
2.6%
2006 454
 
2.1%
2005 450
 
2.1%
2004 429
 
2.0%
2003 422
 
2.0%
2007 415
 
1.9%
1977 415
 
1.9%
1978 384
 
1.8%
1968 379
 
1.8%
2008 367
 
1.7%
Other values (106) 17162
80.1%
ValueCountFrequency (%)
1900 86
0.4%
1901 29
 
0.1%
1902 27
 
0.1%
1903 45
0.2%
1904 44
0.2%
1905 74
0.3%
1906 91
0.4%
1907 65
0.3%
1908 86
0.4%
1909 94
0.4%
ValueCountFrequency (%)
2015 38
 
0.2%
2014 559
2.6%
2013 199
 
0.9%
2012 169
 
0.8%
2011 130
 
0.6%
2010 143
 
0.7%
2009 229
1.1%
2008 367
1.7%
2007 415
1.9%
2006 454
2.1%

yr_renovated
Real number (ℝ)

ZEROS 

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.7298
Minimum0
Maximum2015
Zeros20526
Zeros (%)95.8%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2024-11-27T20:44:19.281306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2015
Range2015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation402.43101
Coefficient of variation (CV)4.7495806
Kurtosis18.610747
Mean84.7298
Median Absolute Deviation (MAD)0
Skewness4.5395471
Sum1816268
Variance161950.72
MonotonicityNot monotonic
2024-11-27T20:44:19.406199image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20526
95.8%
2014 91
 
0.4%
2013 37
 
0.2%
2003 36
 
0.2%
2005 35
 
0.2%
2007 35
 
0.2%
2000 35
 
0.2%
2004 26
 
0.1%
1990 24
 
0.1%
2006 24
 
0.1%
Other values (60) 567
 
2.6%
ValueCountFrequency (%)
0 20526
95.8%
1934 1
 
< 0.1%
1940 2
 
< 0.1%
1944 1
 
< 0.1%
1945 3
 
< 0.1%
1946 2
 
< 0.1%
1948 1
 
< 0.1%
1950 2
 
< 0.1%
1951 1
 
< 0.1%
1953 3
 
< 0.1%
ValueCountFrequency (%)
2015 16
 
0.1%
2014 91
0.4%
2013 37
0.2%
2012 11
 
0.1%
2011 13
 
0.1%
2010 18
 
0.1%
2009 22
 
0.1%
2008 18
 
0.1%
2007 35
 
0.2%
2006 24
 
0.1%

zipcode
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98077.862
Minimum98001
Maximum98199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2024-11-27T20:44:19.529492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98004
Q198033
median98065
Q398117
95-th percentile98177
Maximum98199
Range198
Interquartile range (IQR)84

Descriptive statistics

Standard deviation53.469371
Coefficient of variation (CV)0.00054517268
Kurtosis-0.84968929
Mean98077.862
Median Absolute Deviation (MAD)42
Skewness0.4081288
Sum2.1023971 × 109
Variance2858.9736
MonotonicityNot monotonic
2024-11-27T20:44:19.657879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98103 600
 
2.8%
98038 587
 
2.7%
98115 576
 
2.7%
98052 571
 
2.7%
98117 548
 
2.6%
98042 547
 
2.6%
98034 543
 
2.5%
98118 499
 
2.3%
98023 492
 
2.3%
98006 490
 
2.3%
Other values (60) 15983
74.6%
ValueCountFrequency (%)
98001 359
1.7%
98002 197
0.9%
98003 276
1.3%
98004 315
1.5%
98005 168
 
0.8%
98006 490
2.3%
98007 139
 
0.6%
98008 283
1.3%
98010 99
 
0.5%
98011 194
 
0.9%
ValueCountFrequency (%)
98199 316
1.5%
98198 275
1.3%
98188 135
 
0.6%
98178 258
1.2%
98177 254
1.2%
98168 264
1.2%
98166 250
1.2%
98155 442
2.1%
98148 56
 
0.3%
98146 281
1.3%

lat
Real number (ℝ)

HIGH CORRELATION 

Distinct5034
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.560156
Minimum47.1559
Maximum47.7776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2024-11-27T20:44:19.785422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum47.1559
5-th percentile47.3103
Q147.4711
median47.572
Q347.678
95-th percentile47.749625
Maximum47.7776
Range0.6217
Interquartile range (IQR)0.2069

Descriptive statistics

Standard deviation0.13860131
Coefficient of variation (CV)0.0029142317
Kurtosis-0.67359967
Mean47.560156
Median Absolute Deviation (MAD)0.10475
Skewness-0.4881646
Sum1019499.5
Variance0.019210324
MonotonicityNot monotonic
2024-11-27T20:44:19.907701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.5491 17
 
0.1%
47.6624 17
 
0.1%
47.6846 17
 
0.1%
47.5322 17
 
0.1%
47.6955 16
 
0.1%
47.6711 16
 
0.1%
47.6886 16
 
0.1%
47.6904 15
 
0.1%
47.6647 15
 
0.1%
47.686 15
 
0.1%
Other values (5024) 21275
99.2%
ValueCountFrequency (%)
47.1559 1
< 0.1%
47.1593 1
< 0.1%
47.1622 1
< 0.1%
47.1647 1
< 0.1%
47.1764 1
< 0.1%
47.1775 1
< 0.1%
47.1776 2
< 0.1%
47.1795 1
< 0.1%
47.1803 1
< 0.1%
47.1808 1
< 0.1%
ValueCountFrequency (%)
47.7776 3
< 0.1%
47.7775 3
< 0.1%
47.7774 1
 
< 0.1%
47.7772 3
< 0.1%
47.7771 2
 
< 0.1%
47.777 2
 
< 0.1%
47.7769 3
< 0.1%
47.7768 2
 
< 0.1%
47.7767 6
< 0.1%
47.7766 4
< 0.1%

long
Real number (ℝ)

HIGH CORRELATION 

Distinct752
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.2137
Minimum-122.519
Maximum-121.315
Zeros0
Zeros (%)0.0%
Negative21436
Negative (%)100.0%
Memory size334.9 KiB
2024-11-27T20:44:20.027715image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-122.519
5-th percentile-122.387
Q1-122.328
median-122.23
Q3-122.124
95-th percentile-121.979
Maximum-121.315
Range1.204
Interquartile range (IQR)0.204

Descriptive statistics

Standard deviation0.14089598
Coefficient of variation (CV)-0.0011528657
Kurtosis1.0455319
Mean-122.2137
Median Absolute Deviation (MAD)0.101
Skewness0.88192326
Sum-2619772.8
Variance0.019851678
MonotonicityNot monotonic
2024-11-27T20:44:20.149415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.29 114
 
0.5%
-122.3 110
 
0.5%
-122.362 102
 
0.5%
-122.291 100
 
0.5%
-122.363 99
 
0.5%
-122.372 98
 
0.5%
-122.357 95
 
0.4%
-122.288 95
 
0.4%
-122.365 94
 
0.4%
-122.346 93
 
0.4%
Other values (742) 20436
95.3%
ValueCountFrequency (%)
-122.519 1
 
< 0.1%
-122.515 1
 
< 0.1%
-122.514 1
 
< 0.1%
-122.512 1
 
< 0.1%
-122.511 2
< 0.1%
-122.509 2
< 0.1%
-122.507 1
 
< 0.1%
-122.506 1
 
< 0.1%
-122.505 3
< 0.1%
-122.504 2
< 0.1%
ValueCountFrequency (%)
-121.315 2
< 0.1%
-121.316 1
< 0.1%
-121.319 1
< 0.1%
-121.321 1
< 0.1%
-121.325 1
< 0.1%
-121.352 2
< 0.1%
-121.359 1
< 0.1%
-121.364 2
< 0.1%
-121.402 1
< 0.1%
-121.403 1
< 0.1%

sqft_living15
Real number (ℝ)

HIGH CORRELATION 

Distinct777
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1988.3144
Minimum399
Maximum6210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2024-11-27T20:44:20.262117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum399
5-th percentile1140
Q11490
median1840
Q32370
95-th percentile3300
Maximum6210
Range5811
Interquartile range (IQR)880

Descriptive statistics

Standard deviation685.69909
Coefficient of variation (CV)0.34486452
Kurtosis1.593321
Mean1988.3144
Median Absolute Deviation (MAD)410
Skewness1.1058446
Sum42621507
Variance470183.25
MonotonicityNot monotonic
2024-11-27T20:44:20.372666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1540 193
 
0.9%
1440 190
 
0.9%
1560 190
 
0.9%
1500 179
 
0.8%
1460 168
 
0.8%
1800 166
 
0.8%
1720 165
 
0.8%
1580 165
 
0.8%
1610 164
 
0.8%
1620 163
 
0.8%
Other values (767) 19693
91.9%
ValueCountFrequency (%)
399 1
 
< 0.1%
460 2
 
< 0.1%
620 2
 
< 0.1%
670 1
 
< 0.1%
690 2
 
< 0.1%
700 2
 
< 0.1%
710 2
 
< 0.1%
720 2
 
< 0.1%
740 8
< 0.1%
750 3
 
< 0.1%
ValueCountFrequency (%)
6210 1
 
< 0.1%
6110 1
 
< 0.1%
5790 6
< 0.1%
5610 1
 
< 0.1%
5600 1
 
< 0.1%
5500 1
 
< 0.1%
5380 1
 
< 0.1%
5340 1
 
< 0.1%
5330 1
 
< 0.1%
5220 1
 
< 0.1%

sqft_lot15
Real number (ℝ)

HIGH CORRELATION 

Distinct8689
Distinct (%)40.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12785.961
Minimum651
Maximum871200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size334.9 KiB
2024-11-27T20:44:20.488358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum651
5-th percentile1989.5
Q15100
median7620
Q310087.25
95-th percentile37197
Maximum871200
Range870549
Interquartile range (IQR)4987.25

Descriptive statistics

Standard deviation27375.467
Coefficient of variation (CV)2.1410567
Kurtosis150.32404
Mean12785.961
Median Absolute Deviation (MAD)2508.5
Skewness9.4953799
Sum2.7407987 × 108
Variance7.4941622 × 108
MonotonicityNot monotonic
2024-11-27T20:44:20.604326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 425
 
2.0%
4000 355
 
1.7%
6000 286
 
1.3%
7200 210
 
1.0%
4800 144
 
0.7%
7500 142
 
0.7%
8400 115
 
0.5%
4500 110
 
0.5%
3600 110
 
0.5%
5100 109
 
0.5%
Other values (8679) 19430
90.6%
ValueCountFrequency (%)
651 1
 
< 0.1%
659 1
 
< 0.1%
660 1
 
< 0.1%
748 2
< 0.1%
750 4
< 0.1%
755 1
 
< 0.1%
757 1
 
< 0.1%
758 1
 
< 0.1%
788 1
 
< 0.1%
794 1
 
< 0.1%
ValueCountFrequency (%)
871200 1
< 0.1%
858132 1
< 0.1%
560617 1
< 0.1%
438213 1
< 0.1%
434728 1
< 0.1%
425581 1
< 0.1%
422967 1
< 0.1%
411962 1
< 0.1%
392040 2
< 0.1%
386812 1
< 0.1%

Interactions

2024-11-27T20:44:13.585907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:48.780547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:50.352858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:51.992922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:53.489646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:55.176955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:56.703369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:58.193849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:59.795532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:01.246745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:02.649687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:04.320676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-11-27T20:44:07.339736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-11-27T20:43:59.379599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:00.814617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:02.234493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:03.899315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:05.384418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:06.895556image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:08.434611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:10.197410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:11.680407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:13.152691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:14.714919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:50.022158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:51.646547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:53.146625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:54.831488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:56.362174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:57.844294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:59.470396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:00.910238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:02.324213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:03.990453image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:05.480474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:06.993017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:08.531637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:10.292265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:11.775559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:13.246369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:14.801440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:50.107760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:51.736520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:53.234618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:54.921124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:56.450152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:57.932150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:59.557066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:00.995492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:02.405641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:04.076196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:05.568573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:07.079786image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:08.627393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:10.377355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:11.861979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:13.332416image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:14.882169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:50.191575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:51.823387image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:53.321031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:55.007920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:56.535542image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:58.021207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:59.637345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:01.080732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:02.491460image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:04.162372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:05.650329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:07.169299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:09.002599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:10.461493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:11.948582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:13.419161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:14.965726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:50.276171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:51.909655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:53.407969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:55.094266image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:56.621712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:58.111609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:43:59.717880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:01.164914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:02.573577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:04.243061image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:05.734495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:07.254532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:09.091304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:10.544961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:12.031781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-27T20:44:13.503825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-11-27T20:44:20.694571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
id1.0000.006-0.0180.0010.004-0.013-0.1330.018-0.0030.011-0.0240.006-0.012-0.0060.021-0.017-0.008-0.0030.019-0.004-0.140
date0.0061.000-0.006-0.018-0.036-0.0360.006-0.0240.001-0.002-0.052-0.042-0.029-0.019-0.002-0.0250.003-0.033-0.008-0.0320.002
price-0.018-0.0061.0000.3090.5240.7010.0890.2550.2670.3970.0350.6660.6050.3240.0510.127-0.0510.3070.0190.5840.082
bedrooms0.001-0.0180.3091.0000.5170.5780.0320.175-0.0070.0800.0280.3580.4780.3030.1540.018-0.154-0.0100.1300.3930.029
bathrooms0.004-0.0360.5240.5171.0000.7540.0870.5000.0640.187-0.1280.6650.6850.2840.5050.051-0.2030.0230.2220.5680.087
sqft_living-0.013-0.0360.7010.5780.7541.0000.1720.3530.1040.284-0.0610.7620.8770.4340.3170.055-0.1990.0510.2390.7560.183
sqft_lot-0.1330.0060.0890.0320.0870.1721.000-0.0060.0220.075-0.0090.1130.1830.0150.0520.008-0.129-0.0860.2300.1440.718
floors0.018-0.0240.2550.1750.5000.353-0.0061.0000.0230.028-0.2670.4570.523-0.2460.4890.006-0.0580.0490.1240.279-0.012
waterfront-0.0030.0010.267-0.0070.0640.1040.0220.0231.0000.4030.0170.0830.0720.081-0.0270.0930.031-0.014-0.0420.0870.031
view0.011-0.0020.3970.0800.1870.2840.0750.0280.4031.0000.0450.2500.1670.276-0.0550.1040.0870.006-0.0800.2790.073
condition-0.024-0.0520.0350.028-0.128-0.061-0.009-0.2670.0170.0451.000-0.148-0.1610.174-0.365-0.0610.005-0.015-0.108-0.095-0.004
grade0.006-0.0420.6660.3580.6650.7620.1130.4570.0830.250-0.1481.0000.7560.1670.4450.014-0.1830.1130.1970.7130.118
sqft_above-0.012-0.0290.6050.4780.6850.8770.1830.5230.0720.167-0.1610.7561.000-0.0520.4230.023-0.260-0.0020.3430.7320.193
sqft_basement-0.006-0.0190.3240.3030.2840.4340.015-0.2460.0810.2760.1740.167-0.0521.000-0.1350.0720.0750.109-0.1460.1990.017
yr_built0.021-0.0020.0510.1540.5050.3170.0520.489-0.027-0.055-0.3650.4450.423-0.1351.000-0.226-0.346-0.1490.4090.3250.070
yr_renovated-0.017-0.0250.1270.0180.0510.0550.0080.0060.0930.104-0.0610.0140.0230.072-0.2261.0000.0640.029-0.069-0.0030.008
zipcode-0.0080.003-0.051-0.154-0.203-0.199-0.129-0.0580.0310.0870.005-0.183-0.2600.075-0.3460.0641.0000.267-0.565-0.278-0.147
lat-0.003-0.0330.307-0.0100.0230.051-0.0860.049-0.0140.006-0.0150.113-0.0020.109-0.1490.0290.2671.000-0.1360.048-0.087
long0.019-0.0080.0190.1300.2220.2390.2300.124-0.042-0.080-0.1080.1970.343-0.1460.409-0.069-0.565-0.1361.0000.3340.254
sqft_living15-0.004-0.0320.5840.3930.5680.7560.1440.2790.0870.279-0.0950.7130.7320.1990.325-0.003-0.2780.0480.3341.0000.182
sqft_lot15-0.1400.0020.0820.0290.0870.1830.718-0.0120.0310.073-0.0040.1180.1930.0170.0700.008-0.147-0.0870.2540.1821.000
2024-11-27T20:44:20.892215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
idpricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
id1.0000.0730.0420.0790.0790.0840.0800.0080.0670.0720.1100.0870.0620.1610.0370.3500.2660.3250.1180.095
price0.0731.0000.2960.7420.8980.0390.2060.4190.4610.0550.6560.7910.7930.1140.1670.1760.3410.1470.5430.000
bedrooms0.0420.2961.0000.5140.5780.0000.2060.0000.1000.0620.3730.5050.3590.2150.0160.2020.1100.2080.4400.000
bathrooms0.0790.7420.5141.0000.8720.0780.4680.1330.2670.3020.7170.8460.7310.5910.0780.3350.2430.3410.6140.093
sqft_living0.0790.8980.5780.8721.0000.0980.3610.1830.3410.1420.7510.9120.9160.3680.0930.3130.2090.3000.7440.115
sqft_lot0.0840.0390.0000.0780.0981.0000.0360.0140.0700.0710.0920.1310.0570.0760.0000.0870.1340.1700.0590.628
floors0.0800.2060.2060.4680.3610.0361.0000.0310.0350.2610.3710.4910.2270.6200.0830.3360.2480.3020.3400.035
waterfront0.0080.4190.0000.1330.1830.0140.0311.0000.4900.0140.1530.1070.1760.0440.1440.1040.0450.1260.1170.000
view0.0670.4610.1000.2670.3410.0700.0350.4901.0000.0650.3160.2110.3630.1030.0890.1780.1620.2010.3370.057
condition0.0720.0550.0620.3020.1420.0710.2610.0140.0651.0000.3450.2510.2230.5320.0550.1750.1360.1910.1490.022
grade0.1100.6560.3730.7170.7510.0920.3710.1530.3160.3451.0000.7600.3590.4970.0180.3220.3300.2940.7280.079
sqft_above0.0870.7910.5050.8460.9120.1310.4910.1070.2110.2510.7601.0000.6540.4980.0410.4050.2010.3900.7470.153
sqft_basement0.0620.7930.3590.7310.9160.0570.2270.1760.3630.2230.3590.6541.0000.2870.1020.1900.1570.1880.3830.090
yr_built0.1610.1140.2150.5910.3680.0760.6200.0440.1030.5320.4970.4980.2871.0000.3030.6200.4450.5420.4040.065
yr_renovated0.0370.1670.0160.0780.0930.0000.0830.1440.0890.0550.0180.0410.1020.3031.0000.1120.0770.1080.0000.000
zipcode0.3500.1760.2020.3350.3130.0870.3360.1040.1780.1750.3220.4050.1900.6200.1121.0000.7920.7860.4420.113
lat0.2660.3410.1100.2430.2090.1340.2480.0450.1620.1360.3300.2010.1570.4450.0770.7921.0000.4900.2840.142
long0.3250.1470.2080.3410.3000.1700.3020.1260.2010.1910.2940.3900.1880.5420.1080.7860.4901.0000.4320.194
sqft_living150.1180.5430.4400.6140.7440.0590.3400.1170.3370.1490.7280.7470.3830.4040.0000.4420.2840.4321.0000.086
sqft_lot150.0950.0000.0000.0930.1150.6280.0350.0000.0570.0220.0790.1530.0900.0650.0000.1130.1420.1940.0861.000
2024-11-27T20:44:21.073514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
bathroomsbedroomsconditionfloorsgradeidlatlongpricesqft_abovesqft_basementsqft_livingsqft_living15sqft_lotsqft_lot15viewwaterfrontyr_builtyr_renovatedzipcode
bathrooms1.0000.5220.1300.5460.6570.0140.0070.2600.4950.6900.1910.7450.5700.0690.0630.1140.1020.5660.043-0.203
bedrooms0.5221.0000.0230.2270.3820.006-0.0230.1910.3450.5400.2300.6480.4450.2170.2020.0380.0000.1790.017-0.168
condition0.1300.0231.000-0.291-0.171-0.024-0.023-0.0870.017-0.1610.162-0.065-0.0890.1150.1180.0240.017-0.398-0.067-0.021
floors0.5460.227-0.2911.0000.5010.0180.0240.1480.3200.598-0.2730.4000.305-0.235-0.2320.0230.0220.5520.012-0.060
grade0.6570.382-0.1710.5011.0000.0180.1020.2210.6560.7120.0920.7160.6620.1530.1570.1420.1180.4990.016-0.179
id0.0140.006-0.0240.0180.0181.000-0.0050.0060.0020.0020.0010.000-0.001-0.117-0.1150.0280.0060.026-0.018-0.005
lat0.007-0.023-0.0230.0240.102-0.0051.000-0.1440.456-0.0270.1140.0290.026-0.121-0.1160.0680.034-0.1260.0250.249
long0.2600.191-0.0870.1480.2210.006-0.1441.0000.0610.385-0.2010.2840.3800.3710.3730.0850.0970.411-0.076-0.578
price0.4950.3450.0170.3200.6560.0020.4560.0611.0000.5400.2520.6440.5720.0760.0640.2080.3200.0980.102-0.006
sqft_above0.6900.540-0.1610.5980.7120.002-0.0270.3850.5401.000-0.1670.8440.6970.2730.2550.0890.0820.4710.030-0.277
sqft_basement0.1910.2300.162-0.2730.0920.0010.114-0.2010.252-0.1671.0000.3270.1290.0370.0310.1590.135-0.1800.0630.115
sqft_living0.7450.648-0.0650.4000.7160.0000.0290.2840.6440.8440.3271.0000.7470.3060.2850.1480.1400.3510.052-0.206
sqft_living150.5700.445-0.0890.3050.662-0.0010.0260.3800.5720.6970.1290.7471.0000.3610.3670.1460.0890.334-0.006-0.287
sqft_lot0.0690.2170.115-0.2350.153-0.117-0.1210.3710.0760.2730.0370.3060.3611.0000.9230.0400.014-0.0390.008-0.320
sqft_lot150.0630.2020.118-0.2320.157-0.115-0.1160.3730.0640.2550.0310.2850.3670.9231.0000.0350.000-0.0180.009-0.327
view0.1140.0380.0240.0230.1420.0280.0680.0850.2080.0890.1590.1480.1460.0400.0351.0000.595-0.0680.0960.080
waterfront0.1020.0000.0170.0220.1180.0060.0340.0970.3200.0820.1350.1400.0890.0140.0000.5951.000-0.0290.0920.030
yr_built0.5660.179-0.3980.5520.4990.026-0.1260.4110.0980.471-0.1800.3510.334-0.039-0.018-0.068-0.0291.000-0.215-0.315
yr_renovated0.0430.017-0.0670.0120.016-0.0180.025-0.0760.1020.0300.0630.052-0.0060.0080.0090.0960.092-0.2151.0000.062
zipcode-0.203-0.168-0.021-0.060-0.179-0.0050.249-0.578-0.006-0.2770.115-0.206-0.287-0.320-0.3270.0800.030-0.3150.0621.000

Missing values

2024-11-27T20:44:15.086241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-27T20:44:15.347742image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
071293005202014-10-13221900.031.00118056501.0003711800195509817847.5112-122.25713405650
164141001922014-12-09538000.032.25257072422.000372170400195119919812547.7210-122.31916907639
256315004002015-02-25180000.021.00770100001.000367700193309802847.7379-122.23327208062
324872008752014-12-09604000.043.00196050001.000571050910196509813647.5208-122.39313605000
419544005102015-02-18510000.032.00168080801.0003816800198709807447.6168-122.04518007503
572375503102014-05-121225000.044.5054201019301.00031138901530200109805347.6561-122.0054760101930
613214000602014-06-27257500.032.25171568192.0003717150199509800347.3097-122.32722386819
720080002702015-01-15291850.031.50106097111.0003710600196309819847.4095-122.31516509711
824146001262015-04-15229500.031.00178074701.000371050730196009814647.5123-122.33717808113
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